Robust and Efficient Network Reconstruction in Complex System via Adaptive Signal Lasso

Network reconstruction is important to the understanding and control of collective dynamics in complex systems. Most real networks exhibit sparsely connected properties, and the connection parameter is a signal (0 or 1). Well-known shrinkage methods such as lasso or compressed sensing (CS) to recover structures of complex networks cannot suitably reveal such a property; therefore, the signal lasso method was proposed recently to solve the network reconstruction problem and was found to outperform lasso and CS methods. However, signal lasso suffers the problem that the estimated coefficients that fall between 0 and 1 cannot be successfully selected to the correct class. We propose a new method, adaptive signal lasso, to estimate the signal parameter and uncover the topology of complex networks with a small number of observations. The proposed method has three advantages: (1) It can effectively uncover the network topology with high accuracy and is capable of completely shrinking the signal parameter to either 0 or 1, which eliminates the unclassified portion in network reconstruction; (2) The method performs well in scenarios of both sparse and dense signals and is robust to noise contamination; (3) The method only needs to select one tuning parameter versus two in signal lasso, which greatly reduces the computational cost and is easy to apply. The theoretical properties of this method are studied, and numerical simulations from linear regression, evolutionary games, and Kuramoto models are explored. The method is illustrated with real-world examples from a human behavioral experiment and a world trade web.Comment: 15 pages, 8 figures, 4 table

Comparison of algorithms for road surface temperature prediction

Purpose - The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately. Design/methodology/approach - Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors. Findings - The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms. Originality/value - This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method

Realization of Colored Multicrystalline Silicon Solar Cells with SiO 2

We presented a method to use SiO2/SiNx:H double layer antireflection coatings (DARC) on acid textures to fabricate colored multicrystalline silicon (mc-Si) solar cells. Firstly, we modeled the perceived colors and short-circuit current density (Jsc) as a function of SiNx:H thickness for single layer SiNx:H, and as a function of SiO2 thickness for the case of SiO2/SiNx:H (DARC) with fixed SiNx:H (refractive index n=2.1 at 633 nm, and thickness = 80 nm). The simulation results show that it is possible to achieve various colors by adjusting the thickness of SiO2 to avoid significant optical losses. Therefore, we carried out the experiments by using electron beam (e-beam) evaporation to deposit a layer of SiO2 over the standard SiNx:H for 156×156 mm2 mc-Si solar cells which were fabricated by a conventional process. Semisphere reflectivity over 300 nm to 1100 nm and I-V measurements were performed for grey yellow, purple, deep blue, and green cells. The efficiency of colored SiO2/SiNx:H DARC cells is comparable to that of standard SiNx:H light blue cells, which shows the potential of colored cells in industrial applications

Case report: Treatment of Wilson’s disease by human amniotic fluid administration

BackgroundWilson’s disease (WD) is not an uncommon genetic disease in clinical practice. However, the current WD therapies have limitations. The effectiveness of stem cell therapy in treating WD has yet to be verified, although a few animal studies have shown that stem cell transplantation could partially correct the abnormal metabolic phenotype of WD. In this case report, we present the therapeutic effect of human amniotic fluid containing stem cells in one WD patient.Case presentationA 22-year-old Chinese woman was diagnosed with WD 1 year ago in 2019. The available drugs were not effective in managing the progressive neuropsychiatric symptoms. We treated the patient with pre-cultured human amniotic fluid containing stem cells. Amniotic fluid was collected from pregnant women who underwent induced labor at a gestational age of 19–26 weeks, and then, the fluid was cultured for 2 h to allow stem cell expansion. Cultured amniotic fluid that contained amniotic fluid derived stem cells (AFSC) in the range of approximately 2.8–5.5 × 104/ml was administrated by IV infusion at a rate of 50–70 drops per minute after filtration with a 300-mu nylon mesh. Before the infusion of amniotic fluid, low-molecular-weight heparin and dexamethasone were successively administrated. The patient received a total of 12 applications of amniotic fluid from different pregnant women, and the treatment interval depended on the availability of amniotic fluid. The neuropsychiatric symptoms gradually improved after the stem cell treatment. Dystonia, which included tremor, chorea, dysphagia, dysarthria, and drooling, almost disappeared after 1.5 years of follow-up. The Unified Wilson’s Disease Rating Scale score of the patient decreased from 72 to 10. Brain magnetic resonance imaging (MRI) showed a reduction in the lesion area and alleviation of damage in the central nervous system, along with a partial recovery of the lesion to the normal condition. The serum ceruloplasmin level was elevated from undetectable to 30.8 mg/L, and the 24-h urinary copper excretion decreased from 171 to 37 μg. In addition, amniotic fluid transplantation also alleviates hematopoietic disorders. There were no adverse reactions during or after amniotic fluid administration.ConclusionAmniotic fluid administration, through which stem cells were infused, significantly improves the clinical outcomes in the WD patient, and the finding may provide a novel approach for managing WD effectively

Photoresponse of polyaniline-functionalized graphene quantum dots

Polyaniline-functionalized graphene quantum dots (PANI-GQD) and pristine graphene quantum dots (GQDs) were utilized for optoelectronic devices. The PANI-GQD based photodetector exhibited higher responsivity which is about an order of magnitude at 405 nm and 7 folds at 532 nm as compared to GQD-based photodetectors. The improved photoresponse is attributed to the enhanced interconnection of GQD by island-like polymer matrices, which facilitate carrier transport within the polymer matrices. The optically tunable current–voltage (I–V) hysteresis of PANI-GQD was also demonstrated. The hysteresis magnifies progressively with light intensity at a scan range of ±1 V. Both GQD and PANI-GQD devices change from positive to negative photocurrent when the bias reaches 4 V. Photogenerated carriers are excited to the trapping states in GQDs with increased bias. The trapped charges interact with charges injected from the electrodes which results in a net decrease of free charge carriers and a negative photocurrent. The photocurrent switching phenomenon in GQD and PANI-GQD devices may open up novel applications in optoelectronics